GPU-accelerated Gibbs Sampling. - Alexander Terenin; Shawfeng Dong; David Draper (CoRR, 2016)

Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods. - Alexander Terenin; Eric P. Xing (CoRR, 2017)

A Noninformative Prior on a Space of Distribution Functions. - Alexander Terenin; David Draper (Entropy, 2017)

Variational Integrator Networks for Physically Meaningful Embeddings. - Steindór Sæmundsson; Alexander Terenin; Katja Hofmann; Marc Peter Deisenroth (CoRR, 2019)

Pólya Urn Latent Dirichlet Allocation: A Doubly Sparse Massively Parallel Sampler. - Alexander Terenin; Måns Magnusson; Leif Jonsson; David Draper (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model. - Alexander Terenin; Shawfeng Dong; David Draper (Stat. Comput., 2019)

Variational Integrator Networks for Physically Structured Embeddings. - Steindór Sæmundsson; Alexander Terenin; Katja Hofmann; Marc Peter Deisenroth (AISTATS, 2020)

Asynchronous Gibbs Sampling. - Alexander Terenin; Daniel Simpson; David Draper (AISTATS, 2020)

Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models. - Alexander Terenin; Måns Magnusson; Leif Jonsson (EMNLP (1), 2020)

Efficiently sampling functions from Gaussian process posteriors. - James T. Wilson; Viacheslav Borovitskiy; Alexander Terenin; Peter Mostowsky; Marc Peter Deisenroth (ICML, 2020)

Matérn Gaussian Processes on Riemannian Manifolds. - Viacheslav Borovitskiy; Alexander Terenin; Peter Mostowsky; Marc Peter Deisenroth (NeurIPS, 2020)

Matérn Gaussian Processes on Graphs. - Viacheslav Borovitskiy; Iskander Azangulov; Alexander Terenin; Peter Mostowsky; Marc Peter Deisenroth; Nicolas Durrande (AISTATS, 2021)

Aligning Time Series on Incomparable Spaces. - Samuel Cohen; Giulia Luise; Alexander Terenin; Brandon Amos; Marc Peter Deisenroth (AISTATS, 2021)

Learning Contact Dynamics using Physically Structured Neural Networks. - Andreas Hochlehnert; Alexander Terenin; Steindór Sæmundsson; Marc Peter Deisenroth (AISTATS, 2021)

Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels. - Noémie Jaquier; Viacheslav Borovitskiy; Andrei Smolensky; Alexander Terenin; Tamim Asfour; Leonel Dario Rozo (CoRL, 2021)

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels. - Michael J. Hutchinson; Alexander Terenin; Viacheslav Borovitskiy; So Takao; Yee Whye Teh; Marc Peter Deisenroth (NeurIPS, 2021)

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels. - Michael J. Hutchinson; Alexander Terenin; Viacheslav Borovitskiy; So Takao; Yee Whye Teh; Marc Peter Deisenroth (CoRR, 2021)

Pathwise Conditioning of Gaussian Processes. - James T. Wilson; Viacheslav Borovitskiy; Alexander Terenin; Peter Mostowsky; Marc Peter Deisenroth (J. Mach. Learn. Res., 2021)

Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces. - Alexander Terenin (CoRR, 2022)

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the Compact Case. - Iskander Azangulov; Andrei Smolensky; Alexander Terenin; Viacheslav Borovitskiy (CoRR, 2022)

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. - Jihao Andreas Lin; Javier Antorán; Shreyas Padhy; David Janz; José Miguel Hernández-Lobato; Alexander Terenin (NeurIPS, 2023)

The Cambridge Law Corpus: A Corpus for Legal AI Research. - Andreas Östling; Holli Sargeant; Huiyuan Xie; Ludwig Bull; Alexander Terenin; Leif Jonsson; Måns Magnusson; Felix Steffek (NeurIPS, 2023)

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. - Paul Rosa; Slava Borovitskiy; Alexander Terenin; Judith Rousseau (NeurIPS, 2023)

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces. - Iskander Azangulov; Andrei Smolensky; Alexander Terenin; Viacheslav Borovitskiy (CoRR, 2023)

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. - Paul Rosa; Viacheslav Borovitskiy; Alexander Terenin; Judith Rousseau (CoRR, 2023)

A Unifying Variational Framework for Gaussian Process Motion Planning. - Lucas Cosier; Rares Iordan; Sicelukwanda N. T. Zwane; Giovanni Franzese; James T. Wilson; Marc Peter Deisenroth; Alexander Terenin; Yasemin Bekiroglu (AISTATS, 2024)

Stochastic Gradient Descent for Gaussian Processes Done Right. - Jihao Andreas Lin; Shreyas Padhy; Javier Antorán; Austin Tripp; Alexander Terenin; Csaba Szepesvári; José Miguel Hernández-Lobato; David Janz (ICLR, 2024)

Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index. - Qian Xie 005; Raul Astudillo; Peter I. Frazier; Ziv Scully; Alexander Terenin (NeurIPS, 2024)

The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs. - Peter Mostowsky; Vincent Dutordoir; Iskander Azangulov; Noémie Jaquier; Michael John Hutchinson; Aditya Ravuri; Leonel Rozo; Alexander Terenin; Viacheslav Borovitskiy (CoRR, 2024)

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. - Alexander Terenin; David R. Burt; Artem Artemev; Seth R. Flaxman; Mark van der Wilk; Carl Edward Rasmussen; Hong Ge (J. Mach. Learn. Res., 2024)

An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces. - Alexander Terenin; Jeffrey Negrea (CoRR, 2025)

Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes. - Sidhanth Holalkere; David S. Bindel; Silvia Sellán; Alexander Terenin (CoRR, 2025)

The Gittins Index: A Design Principle for Decision-Making Under Uncertainty. - Ziv Scully; Alexander Terenin (CoRR, 2025)

Cost-aware Stopping for Bayesian Optimization. - Qian Xie 005; Linda Cai; Alexander Terenin; Peter I. Frazier; Ziv Scully (CoRR, 2025)